Hypovolemia diagnosis technique

ABSTRACT

Embodiments of the present disclosure relate to a system and method for determining a risk, onset, or presence of hypovolemia based on one or more features of a plethysmographic waveform during a patient breathing cycle. For example, a hypovolemic patient may exhibit characteristic changes in pulse amplitude or stroke volume during inhalation and exhalation relative to a healthy patient. Further, a trend or pattern of such features may be used to assess the patient&#39;s fluid condition.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 14/509,302,filed Oct. 8, 2014, which is a continuation of U.S. application Ser. No.13/404,402, filed Feb. 24, 2012, U.S. Pat. No. 8,880,155, thedisclosures of which are hereby incorporated by reference in theirentirety for all purposes.

BACKGROUND

The present disclosure relates generally to techniques for monitoringphysiological parameters of a patient. Specifically, embodiments of thepresent disclosure relate to medical devices that are capable ofproviding an indication of hypovolemia.

This section is intended to introduce the reader to various aspects ofart that may be related to various aspects of the present disclosure,which are described and/or claimed below. This discussion is believed tobe helpful in providing the reader with background information tofacilitate a better understanding of the various aspects of the presentdisclosure. Accordingly, it should be understood that these statementsare to be read in this light, and not as admissions of prior art.

One physiological parameter that physicians may wish to monitor is bloodfluid volume (i.e., intravascular volume). Variations from normal fluidvolume in the blood may indicate a change in clinical condition or aninjury. For example, hypovolemia is a state of decreased intravascularvolume that may be associated with dehydration. Correct clinicalassessment of hypovolemia is complex. More specifically, intravascularvolume is difficult to estimate, particularly in critically illpatients. Without an accurate assessment of a patient's intravascularvolume, it is difficult to predict whether a patient will respond tofluid therapy (e.g., a blood or fluid infusion) with an improvement inclinical condition, such as an increase in stroke volume and cardiacoutput. Accordingly, accurate assessments of intravascular volume mayassist a clinician in determining whether a patient will be responsiveto fluid therapy.

To this end, indicators such as the systolic blood pressure variation,pulse pressure variation, or stroke volume variation may be used toestimate intravascular volume and determine whether a patient is likelyto be fluid responsive. However, these measurements tend to be invasive.For example, to obtain an accurate pulse pressure waveform from whichthe intravascular volume can be determined, a physician may insert aninvasive arterial line.

BRIEF DESCRIPTION OF THE DRAWINGS

Advantages of the disclosed techniques may become apparent upon readingthe following detailed description and upon reference to the drawings inwhich:

FIG. 1 is an illustration of a patient monitoring system in accordancewith an embodiment;

FIG. 2 is a block diagram of a patient monitor for determininghypovolemia in accordance with an embodiment;

FIG. 3 is a flow diagram of a method for determining hypovolemia shockin accordance with an embodiment;

FIG. 4 is an illustration of a plethysmographic waveform signal;

FIGS. 5A and 5B show illustrative views of a scalogram derived from aplethysmographic signal in accordance with an embodiment;

FIG. 5C shows an illustrative scalogram derived from a signal containingtwo pertinent components in accordance with an embodiment;

FIG. 5D shows an illustrative schematic of signals associated with aridge in FIG. 5C and illustrative schematics of a further waveletdecomposition of these newly derived signals in accordance with anembodiment; and

FIG. 6 is a block diagram of a closed-loop ventilation system foradministering a fluid therapy in accordance with an embodiment.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

One or more specific embodiments of the present techniques will bedescribed below. In an effort to provide a concise description of theseembodiments, not all features of an actual implementation are describedin the specification. It should be appreciated that in the developmentof any such actual implementation, as in any engineering or designproject, numerous implementation-specific decisions must be made toachieve the developers' specific goals, such as compliance withsystem-related and business-related constraints, which may vary from oneimplementation to another. Moreover, it should be appreciated that sucha development effort might be complex and time consuming, but wouldnevertheless be a routine undertaking of design, fabrication, andmanufacture for those of ordinary skill having the benefit of thisdisclosure.

Photoplethysmography is a noninvasive technique for monitoringphysiological characteristics of a patient. In one example, aphotoplethysmography device uses a sensor that transmits light through apatient's tissue and photoelectrically detects the absorption and/orscattering of the transmitted light in such tissue. More specifically,the light passed through the tissue is typically selected to be of oneor more wavelengths that may be absorbed or scattered by the blood in anamount correlative to the amount of the blood constituent present in theblood. The amount of light absorbed and/or scattered may then be used toestimate the amount of blood constituent in the tissue or the bloodoxygen saturation using various algorithms.

As provided herein, certain features of the plethysmographic waveformmay be analyzed for characteristics, variations, or patterns that arelinked to hypovolemia and, in certain embodiments, hypovolemic shock,e.g., loss of blood volume due to acute trauma or internal bleeding.Based on a detection of low blood volume, appropriate therapy may beadministered to the patient. For example, IV or fluid therapy may beadministered to a patient with hypovolemia. Additionally, the techniquesprovided herein may be used for monitoring surgical or post-surgicalpatients to detect internal bleeding or rapid blood loss.

In particular, the plethysmographic waveform includes a pulsatile ACcomponent that may be characterized by amplitude, width, and/or an areaunder the curve for individual pulses. The AC component of theplethysmographic signal is representative of variations in leftventricular stroke volume during each cardiac cycle, as well as dynamicchanges in the peripheral vasculature. During traumatic shock, there isa loss of blood volume that leads to reductions in left ventricularstroke volume, which in turn may be reflected as characteristic changesin the AC component of the plethysmographic waveform signal. Inaddition, the act of respiration results in characteristic changes inthe AC component of the signal. For example, the inhalation of oxygenand exhalation of carbon dioxide are accompanied by hemodynamic changessuch as beat to beat interval changes, baseline shifts, and pulseamplitude variations. Detection of these changes and analysis of theirmagnitude may be used to monitor the onset or presence of hypovolemicshock. In one embodiment, these waveform features may be calculatedusing algorithms for calculating a patient respiration rate from theplethysmographic waveform.

The changes in the plethysmographic waveform signal that are associatedwith hypovolemia may be detected earlier than other types of changes,including heart rate and blood pressure changes. Accordingly, thepresent techniques allow earlier diagnosis of hypovolemia and/orassociated shock. In addition, the present techniques account for thepresence of arrhythmic beats. Rather than ignoring the presence ofarrhythmia, the present techniques may use the number, frequency, ortype of arrhythmic beats as a factor in determining the probability ofshock. For example, in one embodiment, a determination of hypovolemicshock may be based on the respiration rate pulse band, respiratory sinusarrhythmia, and baseline modulation during respiration.

With this in mind, FIG. 1 depicts an embodiment of a patient monitoringsystem 10 that may be used in conjunction with a plethysmographic sensor12. Although the depicted embodiments relate to relate tophotoplethysmography or pulse oximetry, the system 10 may be configuredto obtain a variety of medical measurements with a suitable medicalsensor. The system 10 includes the sensor 12 that is communicativelycoupled to a patient monitor 14. The sensor 12 includes one or moreemitters 16 and one or more detectors 18. The emitters 16 and detectors18 of the sensor 12 are coupled to the monitor 14 via a cable 24 througha plug 25 coupled to a sensor port. Additionally, the monitor 14includes a monitor display 20 configured to display informationregarding the physiological parameters, information about the system,and/or alarm indications. The monitor 14 may include various inputcomponents 22, such as knobs, switches, keys and keypads, buttons, etc.,to provide for operation and configuration of the monitor. The monitor14 also includes a processor that may be used to execute code such ascode for implementing the techniques discussed herein.

The monitor 14 may be any suitable monitor, such as a pulse oximetrymonitor available from Nellcor Puritan Bennett LLC. The monitor 14 mayalso be capable of determining a patient's respiration rate based on theplethysmographic waveform signal. Furthermore, to upgrade conventionaloperation provided by the monitor 14 to provide additional functions,the monitor 14 may be coupled to a multi-parameter patient monitor 26via a cable 32 connected to a sensor input port or via a cable 36connected to a digital communication port, or via an RF or opticalwireless link. Alternatively, the techniques provided herein may beincorporated into one or more individual modules with plug-inconnectivity to the multi-parameter patient monitor 26. Such modules mayinclude connectors that allow the calculated physiological parameters tobe sent to the host multi-parameter monitor. In addition, the monitor14, or, alternatively, the multi-parameter patient monitor 26, may beconfigured to calculate physiological parameters and to provide acentral display 28 for the visualization of information from the monitor14 and from other medical monitoring devices or systems. Themulti-parameter monitor 26 includes a processor that may be configuredto execute code. The multi-parameter monitor 26 may also include variousinput components 30, such as knobs, switches, keys and keypads, buttons,etc., to provide for operation and configuration of the amulti-parameter monitor 26. In addition, the monitor 14 and/or themulti-parameter monitor 26 may be connected to a network to enable thesharing of information with servers or other workstations. In certainembodiments, the sensor 12 may be a wireless sensor 12. Accordingly, thewireless sensor 12 may establish a wireless communication with thepatient monitor 14 and/or the multi-parameter patient monitor 26 usingany suitable wireless standard. By way of example, the wireless modulemay be capable of communicating using one or more of the ZigBeestandard, WirelessHART standard, Bluetooth standard, IEEE 802.11xstandards, or MiWi standard. In embodiments in which the sensor 12 isconfigured for wireless communication, the strain relief features of thecable 24 may be housed in the sensor body 34.

As provided herein, the sensor 12 may be a sensor suitable for detectionof one or more physiological parameters. The sensor 12 may includeoptical components (e.g., one or more emitters 16 and detectors 18). Inone embodiment, the sensor 12 may be configured for photo-electricdetection of blood and tissue constituents. For example, the sensor 12may include pulse oximetry sensing functionality for determining theoxygen saturation of blood as well as other parameters (e.g.,respirations rate) from the plethysmographic waveform detected by theoximetry technique. An oximetry system may include a light sensor (e.g.,sensor 12) that is placed at a site on a patient, typically a fingertip,toe, forehead or earlobe, or in the case of a neonate, across a foot.The sensor 12 may pass light using the emitter 16 through blood perfusedtissue and photoelectrically sense the absorption of light in thetissue. For example, the monitor 14 may measure the intensity of lightthat is received at the light sensor as a function of time. A signalrepresenting light intensity versus time or a mathematical manipulationof this signal (e.g., a scaled version thereof, a log taken thereof, ascaled version of a log taken thereof, etc) may be referred to as thephotoplethysmograph (PPG) signal. The light intensity or the amount oflight absorbed may then be used to calculate the amount of the bloodconstituent (e.g., oxyhemoglobin) being measured and other physiologicalparameters such as the pulse rate and when each individual pulse occurs.Generally, the light passed through the tissue is selected to be of oneor more wavelengths that are absorbed by the blood in an amountrepresentative of the amount of the blood constituent present in theblood. The amount of light passed through the tissue varies inaccordance with the changing amount of blood constituent in the tissueand the related light absorption. At least two, e.g., red and infrared(IR), wavelengths may be used because it has been observed that highlyoxygenated blood will absorb relatively less red light and more infraredlight than blood with a lower oxygen saturation. However, it should beunderstood that any appropriate wavelengths, e.g., green, etc., may beused as appropriate. Further, photoplethysmography measurements may bedetermined based on one, two, or three or more wavelengths of light.

Turning to FIG. 2, a simplified block diagram of the medical system 10is illustrated in accordance with an embodiment. As noted, the sensor 12may include optical components in the forms of emitters 16 and detectors18. The emitter 16 and the detector 18 may be arranged in a reflectanceor transmission-type configuration with respect to one another. However,in embodiments in which the sensor 12 is configured for use on apatient's forehead (e.g. either alone or in conjunction with a hat orheadband), the emitters 16 and detectors 18 may be in a reflectanceconfiguration. Such sensors 12 may be used for pulse oximetry orregional saturation monitoring (e.g., INVOS® monitoring). An emitter 16may also be a light emitting diode, superluminescent light emittingdiode, a laser diode or a vertical cavity surface emitting laser(VCSEL). An emitter 16 and detector 18 may also include optical fibersensing elements. An emitter 16 may include a broadband or “white light”source, in which case the detector could include any of a variety ofelements for selecting specific wavelengths, such as reflective orrefractive elements, absorptive filters, dielectric stack filters, orinterferometers. These kinds of emitters and/or detectors wouldtypically be coupled to the sensor 12 via fiber optics. Alternatively, asensor assembly 12 may sense light detected from the tissue is at adifferent wavelength from the light emitted into the tissue. Suchsensors may be adapted to sense fluorescence, phosphorescence, Ramanscattering, Rayleigh scattering and multi-photon events or photoacousticeffects in conjunction with the appropriate sensing elements.

In certain embodiments, the emitter 16 and detector 18 may be configuredfor pulse oximetry. It should be noted that the emitter 16 may becapable of emitting at least two wavelengths of light, e.g., red andinfrared (IR) light, into the tissue of a patient, where the redwavelength may be between about 600 nanometers (nm) and about 700 nm,and the IR wavelength may be between about 800 nm and about 1000 nm. Theemitter 16 may include a single emitting device, for example, with twoLEDs, or the emitter 16 may include a plurality of emitting deviceswith, for example, multiple LED's at various locations. In someembodiments, the LEDs of the emitter 16 may emit three or more differentwavelengths of light. Such wavelengths may include a red wavelength ofbetween approximately 620-700 nm (e.g., 660 nm), a far red wavelength ofbetween approximately 690-770 nm (e.g., 730 nm), and an infraredwavelength of between approximately 860-940 nm (e.g., 900 nm). Otherwavelengths may include, for example, wavelengths of betweenapproximately 500-600 nm and/or 1000-1100 nm and/or 1200-1400 nm.Regardless of the number of emitting devices, light from the emitter 16may be used to measure, as provided herein, a risk, onset, or presenceof hypovolemia. In certain embodiments, the sensor measurements may alsobe used for determining oxygen saturation, respiration rate, waterfraction, hematocrit, or other physiologic parameters of the patient. Itshould be understood that, as used herein, the term “light” may refer toone or more of ultrasound, radio, microwave, millimeter wave, infrared,visible, ultraviolet, gamma ray or X-ray electromagnetic radiation, andmay also include any wavelength within the radio, microwave, infrared,visible, ultraviolet, or X-ray spectra, and that any suitable wavelengthof light may be appropriate for use with the present disclosure. Inanother embodiment, two emitters 16 may be configured for use in aregional saturation technique. To that end, the emitters 16 may includetwo light emitting diodes (LEDs) that are capable of emitting at leasttwo wavelengths of light, e.g., red or near infrared light. In oneembodiment, the LEDs emit light in the range of 600 nanometers toapproximately 1000 nm. In a particular embodiment, one LED is capable ofemitting light at 730 nm and the other LED is capable of emitting lightat 810 nm.

In any suitable configuration of the sensor 12, the detector 18 may bean array of detector elements that may be capable of detecting light atvarious intensities and wavelengths. In one embodiment, light enters thedetector 18 after passing through the tissue of the patient. In anotherembodiment, light emitted from the emitter 16 may be reflected byelements in the patient's tissue to enter the detector 18. The detector18 may convert the received light at a given intensity, which may bedirectly related to the absorbance and/or reflectance of light in thetissue of the patient, into an electrical signal. That is, when morelight at a certain wavelength is absorbed, less light of that wavelengthis typically received from the tissue by the detector 18, and when morelight at a certain wavelength is reflected, more light of thatwavelength is typically received from the tissue by the detector 18. Thedetector 18 may receive light that has not entered the tissue to be usedas a reference signal. After converting the received light to anelectrical signal, the detector 18 may send the signal to the monitor14, where physiological characteristics may be calculated based at leastin part on the absorption and/or reflection of light by the tissue ofthe patient.

In certain embodiments, the medical sensor 12 may also include anencoder 47 that may provide signals indicative of the wavelength of oneor more light sources of the emitter 16, which may allow for selectionof appropriate calibration coefficients for calculating a physicalparameter such as blood oxygen saturation. The encoder 47 may, forinstance, be a coded resistor, EEPROM or other coding devices (such as acapacitor, inductor, PROM, RFID, parallel resident currents, or acolorimetric indicator) that may provide a signal to a microprocessor 48related to the characteristics of the medical sensor 12 to enable themicroprocessor 48 to determine the appropriate calibrationcharacteristics of the medical sensor 12. Further, the encoder 47 mayinclude encryption coding that prevents a disposable part of the medicalsensor 12 from being recognized by a microprocessor 48 unable to decodethe encryption. For example, a detector/decoder 49 may translateinformation from the encoder 47 before it can be properly handled by theprocessor 48. In some embodiments, the encoder 47 and/or thedetector/decoder 48 may not be present. In some embodiments, theencrypted information held by the encoder 47 may itself be transmittedvia an encrypted data protocol to the detector/decoder 49, such that thecommunication between 47 and 49 is secured.

Signals from the detector 18 and/or the encoder 47 may be transmitted tothe monitor 14. The monitor 14 may include one or more processors 48coupled to an internal bus 50. Also connected to the bus may be a RUMmemory 52, a RAM memory 54, non-volatile memory 56, a display 20, andcontrol inputs 22. A time processing unit (TPU) 58 may provide timingcontrol signals to light drive circuitry 60, which controls when theemitter 16 is activated, and if multiple light sources are used, themultiplexed timing for the different light sources. TPU 58 may alsocontrol the gating-in of signals from detector 18 through a switchingcircuit 64. These signals are sampled at the proper time, depending atleast in part upon which of multiple light sources is activated, ifmultiple light sources are used. The received signal from the detector18 may be passed through one or more amplifiers (e.g., amplifiers 62 and66), a low pass filter 68, and an analog-to-digital converter 70 foramplifying, filtering, and digitizing the electrical signals from thesensor 12. The digital data may then be stored in a queued serial module(QSM) 72, for later downloading to RAM 54 as QSM 72 fills up. In anembodiment, there may be multiple parallel paths for separateamplifiers, filters, and A/D converters for multiple light wavelengthsor spectra received.

Based at least in part upon the received signals corresponding to thelight received by optical components of the pulse oximetry sensor 20,microprocessor 48 may calculate a hypovolemia indicator and/or therespiration rate, oxygen saturation and/or heart rate using variousalgorithms, such as those employed by the Nellcor™ N-600x™ pulseoximetry monitor, which may be used in conjunction with various Nellcor™pulse oximetry sensors, such as OxiMax™ sensors. In addition, themicroprocessor 48 may calculate and/or display trend or parametervariability using various methods, such as those provided herein. Thesealgorithms may employ certain coefficients, which may be empiricallydetermined, and may correspond to the wavelengths of light used. Thealgorithms and coefficients may be stored in a RUM 52 or other suitablecomputer-readable storage medium and accessed and operated according tomicroprocessor 48 instructions. In one embodiment, the correctioncoefficients may be provided as a lookup table.

As provided herein, a loss or change in intravascular volume associatedwith hypovolemia may be determined based on changes or patterns detectedin the characteristic respiratory-induced variations of theplethysmographic waveform. In particular embodiments, these changes maybe assessed using algorithms for determining respiration rate and/oroxygen saturation. For example, a patient monitor 14 capable ofdetermining a respiration rate may employ certain algorithms that assessfeatures of the waveform including amplitude variations, baselineshifts, arrhythmic beats, and beat-to-beat variations. These featuresmay be extracted from the algorithm calculations and used as inputs to ahypovolemia determination. In another embodiment, signal processingtechniques used for processing the plethysmographic waveform signal todetermine oxygen saturation may be used to assess pulse quality, changesin pulse amplitude, and/or pulse shape. Further, in particularembodiments, information about a patient's breathing cycle may also beused as an input to a hypovolemia determination.

For example, a patient's pulse amplitude may be assessed to determine ifthe patient is hypovolemic. For healthy patients with normal fluidstatus, the left ventricular stroke volume decreases as the patientbreathes in. During exhalation, there is a corresponding small increasein the left ventricular stroke volume. These changes in stroke volumerelate to respiratory-induced pulse amplitude variations in theplethysmographic waveform. Accordingly, changes in pulse amplitude maybe used at least in part to estimate changes in stroke volume. Incertain embodiments, the estimation of stroke volume may alsoincorporate additional factors and/or parameters, such as heart rate,vascular tone, or pulse pressure. For patients experiencing onset ofhypovolemia, a relatively larger drop in stroke volume during inhalationmay be observed. Similarly, a relatively larger increase in strokevolume occurs during exhalation for patients with hypovolemia. Bytracking trends in pulse amplitude as correlated to a breathing cycle,early onset of hypovolemia may be detected.

FIG. 3 is a process flow diagram illustrating a method 80 in accordancewith some embodiments. The method may be performed as an automatedprocedure by a system, such as system 10. In addition, certain steps ofthe method may be performed by a processor, or a processor-based devicesuch as a patient monitor 14 that includes instructions for implementingcertain steps of the method 80. According to an embodiment, the method80 begins with obtaining a plethysmographic waveform signal from a pulseoximetry sensor 12 at step 88.

The monitor 14 may perform assess one or more features of theplethysmographic waveform at step 90 based on the plethysmographicwaveform signal obtained at step 88. At step 92, the plethysmographicwaveform signal and its features are correlated to information relatedto a breathing cycle of a patient. The information about the breathingcycle may be determined based on an analysis of the plethysmographicwaveform signal. That is, based on certain patterns associated withrespiration and their effects on the plethysmographic waveform signal,the pulses may be correlated to a full breathing cycle includinginspiration and exhalation or only certain portions of the breathingcycle (e.g., the inhalation and exhalation components of the signal maybe extracted). Measuring respiration cycles may be performed usingtechniques such as those provided in U.S. Patent Publication No.2010/0324827, which is incorporated by reference herein in its entiretyfor all purposes. Other inputs used in the correlation may be apatient's calculated heart rate and respiration rate. In a particularembodiment, if the patient is receiving breathing assistance, aventilator may provide input to the system 10 to correlate the breathingcycle with the plethysmographic waveform. For patients breathing withoutassistance, a breathing cycle may be determined by a nasal thermistor,which measures the temperature changes that occur during inhalation andexhalation. Other techniques may involve indirect measurements ofchanges in body volume as a result of respiration, includingtransthoracic inductance, impedance plethysmography, or strain gaugemeasurement of thoracic circumference. Time-stamped inputs from suchsensors or monitors may be provided to the monitor 14 and correlated toevents in the plethysmographic waveform.

Based on patients associated with healthy fluid levels and/orhypovolemia, the monitor 14 may calculate an assessment of the patient'sfluid condition, i.e., a risk of hypovolemia at step 94, and provide adisplay or other indication to a clinician, such as a graphical, visual,or audio representation of the intravascular volume at step 96. In oneembodiment, a blood volume indicator may represent the presence ofmeasurements, patterns, or trends in the plethysmographic waveformsignal associated with normal intravascular blood volume and may includea numeric value or a green light indicated on a display or a short tonegenerated by a speaker associated with monitor 14. Similarly,measurements, patterns, or trends in the plethysmographic waveformsignal associated with hypovolemia may trigger an alarm, which mayinclude one or more of an audio or visual alarm indication. Further, themonitor 14 may provide a confidence metric or indicator to provideinformation to the clinician relating to how may parameters may havebeen taken into account. For example, if the indicator is takes intoaccount the presence of arrhythmia or arrhythmic beats, the confidencemay be higher than if arrhythmia is not accounted for.

In one embodiment, the alarm may be triggered if the hypovolemiaindicator is substantially greater than a predetermined value,substantially less than a predetermined value, or outside of apredetermined range. The predetermined values, thresholds, and/or rangesmay be empirically determined based on clinical observations of patientswith normal fluid status relative to hypovolemic patients. In aparticular embodiment, a hypovolemia indicator may increase ifparticular trends in the plethysmographic waveform are detected. Onesuch pattern may be a trending increase in a patient's stroke volumeduring exhalation. Another such trend may be a trending decrease in apatient's stroke volume during inhalation. These two trends incombination with one another may also be used to increase confidence forthe hypovolemia indicator. Further, trending increases or decreases maybe assessed over one or more breathing cycles. For example, maintainingan increase or decrease in a trending value over multiple breath cyclesmay also increase confidence for the hypovolemia indicator. The trendvalues may be relative to previously assessed values in the patient ormay be relative to empirically-determined cutoff values. In oneembodiment, the monitor 14 may employ a truth table in which hypovolemiais indicated if both trends are present for a minimum time period. Inanother example, each incidence of a change in stroke volume outside ofa normal threshold may be added to a counter, which may trigger an alarmwhen the counter reaches a minimum count.

In one embodiment, the disclosed techniques for monitoring hypovolemiamay be used during a surgical procedure. In such an embodiment, aplurality of baseline measurements of the characteristics of theplethysmographic waveform may be recorded, for example over a five orten minute period prior to the surgery. These baseline measurements maythen be used to compute a baseline value of, for example, pulseamplitude during inhalation and exhalation. During and post-surgery, anytrends of pulse amplitude (or any other suitable characteristic of theplethysmographic waveform) that are associated with hypovolemia may beassessed. As noted, a trend of increasing pulse amplitude duringexhalation relative to a baseline or pre-surgery level may be indicativeof hypovolemia. Because hypovolemia may be a precursor to hypovolemicshock, early detection may lead to timely fluid treatment and improvedsurgical outcomes.

FIG. 4 illustrates particular features or characteristics of aplethysmographic waveform 100 that may be assessed in determining if apatient is at risk of developing hypovolemia. An individual peak 102 maybe characterized by its peak 104 and its trough 106. In addition, theabsolute distance 108 between the peak 104 and the trough 106 and thearea under the curve 110 may be assessed. The plethysmographic waveformmay also be assessed by its peak-to-peak variation 112, trough-to-troughvariation 114, or a change in absolute distance 108 from beat-to-beat.In one embodiment, a pulse amplitude may be assessed as the absolutedistance 108. In addition, a time window 116 for assessing changes ortrends may be selected according to the desired monitoring parameters.For example, an operator may increase the size of the window 116 from 10seconds to 30 seconds to capture more data. The monitor 14 may providerolling updates as the window 116 moves forward in time.

The plethysmographic waveform 100 is pulsatile and reflects hemodynamicproperties, which may be used to determine a patient's blood oxygensaturation as well as heart rate. In addition, the plethysmographicwaveform may be used to determine a patient's respiration rate. In oneembodiment, the respiratory rate may be determined by analyzing awavelet transformed plethysmographic signal. Information derived fromthe transform of the plethysmographic signal (i.e., in wavelet space)may be used to provide measurements of one or more physiologicalparameters.

The continuous wavelet transform of a signal x(t) in accordance with thepresent disclosure may be defined as

$\begin{matrix}{{T\left( {a,b} \right)} = {\frac{1}{\sqrt{a}}{\int_{- \infty}^{+ \infty}{{x(t)}{\psi^{*}\left( \frac{t - b}{a} \right)}\ {\mathbb{d}t}}}}} & (1)\end{matrix}$where ψ*(t) is the complex conjugate of the wavelet function ψ(t), a isthe dilation parameter of the wavelet and b is the location parameter ofthe wavelet. The transform given by equation (1) may be used toconstruct a representation of a signal on a transform surface. Thetransform may be regarded as a time-scale representation. Wavelets arecomposed of a range of frequencies, one of which may be denoted as thecharacteristic frequency of the wavelet, where the characteristicfrequency associated with the wavelet is inversely proportional to thescale a. One example of a characteristic frequency is the dominantfrequency. Each scale of a particular wavelet may have a differentcharacteristic frequency. The underlying mathematical detail requiredfor the implementation within a time-scale can be found, for example, inPaul S. Addison, The Illustrated Wavelet Transform Handbook (Taylor &Francis Group 2002), which is hereby incorporated by reference herein inits entirety.

The continuous wavelet transform decomposes a signal using wavelets,which are generally highly localized in time. The continuous wavelettransform may provide a higher resolution relative to discretetransforms, thus providing the ability to garner more information fromsignals than typical frequency transforms such as Fourier transforms (orany other spectral techniques) or discrete wavelet transforms.Continuous wavelet transforms allow for the use of a range of waveletswith scales spanning the scales of interest of a signal such that smallscale signal components correlate well with the smaller scale waveletsand thus manifest at high energies at smaller scales in the transform.Likewise, large scale signal components correlate well with the largerscale wavelets and thus manifest at high energies at larger scales inthe transform. Thus, components at different scales may be separated andextracted in the wavelet transform domain. Moreover, the use of acontinuous range of wavelets in scale and time position allows for ahigher resolution transform than is possible relative to discretetechniques.

In addition, transforms and operations that convert a signal or anyother type of data into a spectral (i.e., frequency) domain necessarilycreate a series of frequency transform values in a two-dimensionalcoordinate system where the two dimensions may be frequency and, forexample, amplitude. For example, any type of Fourier transform wouldgenerate such a two-dimensional spectrum. In contrast, wavelettransforms, such as continuous wavelet transforms, are required to bedefined in a three-dimensional coordinate system and generate a surfacewith dimensions of time, scale and, for example, amplitude. Hence,operations performed in a spectral domain cannot be performed in thewavelet domain; instead the wavelet surface must be transformed into aspectrum (i.e., by performing an inverse wavelet transform to convertthe wavelet surface into the time domain and then performing a spectraltransform from the time domain). Conversely, operations performed in thewavelet domain cannot be performed in the spectral domain; instead aspectrum must first be transformed into a wavelet surface (i.e., byperforming an inverse spectral transform to convert the spectral domaininto the time domain and then performing a wavelet transform from thetime domain). Nor does a cross-section of the three-dimensional waveletsurface along, for example, a particular point in time equate to afrequency spectrum upon which spectral-based techniques may be used. Atleast because wavelet space includes a time dimension, spectraltechniques and wavelet techniques are not interchangeable. It will beunderstood that converting a system that relies on spectral domainprocessing to one that relies on wavelet space processing would requiresignificant and fundamental modifications to the system in order toaccommodate the wavelet space processing (e.g., to derive arepresentative energy value for a signal or part of a signal requiresintegrating twice, across time and scale, in the wavelet domain while,conversely, one integration across frequency is required to derive arepresentative energy value from a spectral domain). As a furtherexample, to reconstruct a temporal signal requires integrating twice,across time and scale, in the wavelet domain while, conversely, oneintegration across frequency is required to derive a temporal signalfrom a spectral domain. It is well known in the art that, in addition toor as an alternative to amplitude, parameters such as energy density,modulus, phase, among others may all be generated using such transformsand that these parameters have distinctly different contexts andmeanings when defined in a two-dimensional frequency coordinate systemrather than a three-dimensional wavelet coordinate system. For example,the phase of a Fourier system is calculated with respect to a singleorigin for all frequencies while the phase for a wavelet system isunfolded into two dimensions with respect to a wavelet's location (oftenin time) and scale.

The energy density function of the wavelet transform, the scalogram, isdefined asS(a,b)=|T(a,b)|²   (2)where ‘∥’ is the modulus operator. The scalogram may be resealed foruseful purposes. One common resealing is defined as

$\begin{matrix}{{S_{R}\left( {a,b} \right)} = \frac{\left| {T\left( {a,b} \right)} \right|^{2}}{a}} & (3)\end{matrix}$and is useful for defining ridges in wavelet space when, for example,the Morlet wavelet is used. Ridges are defined as the locus of points oflocal maxima in the plane. Any reasonable definition of a ridge may beemployed in the method. Also included as a definition of a ridge hereinare paths displaced from the locus of the local maxima. A ridgeassociated with only the locus of points of local maxima in the plane islabeled a “maxima ridge”.

For implementations involving fast numerical computation, the wavelettransform may be expressed as an approximation using Fourier transforms.Pursuant to the convolution theorem, because the wavelet transform isthe cross-correlation of the signal with the wavelet function, thewavelet transform may be approximated in terms of an inverse FFT of theproduct of the Fourier transform of the signal and the Fourier transformof the wavelet for each required a scale and then multiplying the resultby √(a).

In the discussion of the techniques herein, the “scalogram” may be takento include all suitable forms of resealing including, but not limitedto, the original unsealed wavelet representation, linear resealing, anypower of the modulus of the wavelet transform, or any other suitableresealing. In addition, for purposes of clarity and conciseness, theterm “scalogram” shall be taken to mean the wavelet transform, T(a,b)itself, or any part thereof. For example, the real part of the wavelettransform, the imaginary part of the wavelet transform, the phase of thewavelet transform, any other suitable part of the wavelet transform, orany combination thereof is intended to be conveyed by the term“scalogram”.

A scale, which may be interpreted as a representative temporal period,may be converted to a characteristic frequency of the wavelet function.The characteristic frequency associated with a wavelet of arbitrary ascale is given by

$\begin{matrix}{f = \frac{f_{c}}{a}} & (4)\end{matrix}$

where f_(c) the characteristic frequency of the mother wavelet (i.e., ata=1), becomes a scaling constant and f is the representative orcharacteristic frequency for the wavelet at arbitrary scale a.

Any suitable wavelet function may be used in connection with the presentdisclosure. One of the most commonly used complex wavelets, the Monletwavelet, is defined as:ψ(t)=π^(−1/4)(e ^(12πf) ⁰ ^(t) −e ^(−(2πf) ⁰ ⁾ ² ^(/2))e ^(−t) ² ^(/2)  (5)where f₀ is the central frequency of the mother wavelet. The second termin the parenthesis is known as the correction term, as it corrects forthe non-zero mean of the complex sinusoid within the Gaussian window. Inpractice, it becomes negligible for values of f₀>>0 and can be ignored,in which case, the Morlet wavelet can be written in a simpler form as

$\begin{matrix}{{\psi(t)} = {\frac{1}{\pi^{1\text{/}4}}e^{i\; 2\pi\; f_{0}t}e^{{- t^{2}}\text{/}2}}} & (6)\end{matrix}$This wavelet is a complex wave within a scaled Gaussian envelope. Whileboth definitions of the Morlet wavelet are included herein, the functionof equation (6) is not strictly a wavelet as it has a non-zero mean(i.e., the zero frequency term of its corresponding energy spectrum isnon-zero). However, it will be recognized by those skilled in the artthat equation (6) may be used in practice with f₀>>0 with minimal errorand is included (as well as other similar near wavelet functions) in thedefinition of a wavelet herein. A more detailed overview of theunderlying wavelet theory, including the definition of a waveletfunction, can be found in the general literature. Discussed herein ishow wavelet transform features may be extracted from the waveletdecomposition of signals. For example, wavelet decomposition ofplethysmographic signals may be used to provide clinically usefulinformation within a medical device.

Pertinent repeating features in a signal give rise to a time-scale bandin wavelet space or a resealed wavelet space. For example, the pulsecomponent of a plethysmographic signal produces a dominant band inwavelet space at or around the pulse frequency. FIGS. 5A and B show twoviews of an illustrative scalogram 150 derived from a plethysmographicsignal, according to an embodiment. The figures show an example of theband 152 caused by the pulse component in such a signal. The pulse bandis located between the dashed lines in the plot of FIG. 5A. The band isformed from a series of dominant coalescing features across thescalogram. This can be clearly, seen as a raised band across thetransform surface in FIG. 5B located within the region of scalesindicated by the arrow in the plot (corresponding to 60 beats perminute). The maximum of this band with respect to scale is the ridge.The locus of the ridge is shown as a black curve on top of the band inFIG. 5B. By employing a suitable resealing of the scalogram, such asthat given in equation (3), the ridges found in wavelet space may berelated to the instantaneous frequency of the signal. In this way, thepulse rate may be obtained from the plethysmographic signal. Instead ofresealing the scalogram, a suitable predefined relationship between thescale obtained from the ridge on the wavelet surface and the actualpulse rate may also be used to determine the pulse rate.

By mapping the time-scale coordinates of the pulse ridge onto thewavelet phase information gained through the wavelet transform,individual pulses may be captured. In this way, both times betweenindividual pulses and the timing of components within each pulse may bemonitored and used to detect heart beat anomalies, measure arterialsystem compliance, or perform any other suitable calculations ordiagnostics. Alternative definitions of a ridge may be employed.Alternative relationships between the ridge and the pulse frequency ofoccurrence may be employed.

As discussed above, pertinent repeating features in the signal give riseto a time-scale band in wavelet space or a rescaled wavelet space. For aperiodic signal, this band remains at a constant scale in the time-scaleplane. For many real signals, especially biological signals, the bandmay be non-stationary; varying in scale, amplitude, or both over time.FIG. 5C shows an illustrative schematic of a wavelet transform of asignal containing two pertinent components leading to two bands in thetransform space, according to an embodiment. These bands are labeledband A and band B on the three-dimensional schematic of the waveletsurface. In this embodiment, the band ridge is defined as the locus ofthe peak values of these bands with respect to scale. For purposes ofdiscussion, it may be assumed that band B contains the signalinformation of interest. This will be referred to as the “primary band.”In addition, it may be assumed that the system from which the signaloriginates, and from which the transform is subsequently derived,exhibits some form of coupling between the signal components in band Aand band B. When noise or other erroneous features are present in thesignal with similar spectral characteristics of the features of band Bthen the information within band B can become ambiguous obscured,fragmented or missing). In this case, the ridge of band A may befollowed in wavelet space and extracted either as an amplitude signal ora scale signal which will be referred to as the “ridge amplitudeperturbation” (RAP) signal and the “ridge scale perturbation” (RSP)signal, respectively. The RAP and RSP signals may be extracted byprojecting the ridge onto the time-amplitude or time-scale planes,respectively. The top plots of FIG. 5D show a schematic of the RAP andRSP signals associated with ridge A in FIG. 5C. Below these RAP and RSPsignals are schematics of a further wavelet decomposition of these newlyderived signals. This secondary wavelet decomposition allows forinformation in the region of band B in FIG. 5C to be made available asband C and band D. The ridges of bands C and D may serve asinstantaneous time-scale characteristic measures of the signalcomponents causing bands C and D. This technique, which will be referredto herein as secondary wavelet feature decoupling (SWFD), may allowinformation concerning the nature of the signal components associatedwith the underlying physical process causing the primary band B (FIG.5C) to be extracted when band B itself is obscured in the presence ofnoise or other erroneous signal features.

In some instances, an inverse continuous wavelet transform may bedesired, such as when modifications to a scalogram (or modifications tothe coefficients of a transformed signal) have been made in order to,for example, remove artifacts. In one embodiment, there is an inversecontinuous wavelet transform which allows the original signal to berecovered from its wavelet transform by integrating over all scales andlocations, a and b:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}\frac{1}{\sqrt{a}}{\psi\left( \frac{t - b}{a} \right)}\frac{{\mathbb{d}a}{\mathbb{d}b}}{a^{2}}}}}}} & (7)\end{matrix}$which may also be written as:

$\begin{matrix}{{x(t)} = {\frac{1}{C_{g}}{\int_{- \infty}^{\infty}{\int_{0}^{\infty}{{T\left( {a,b} \right)}{\psi_{a,b}(t)}\frac{{\mathbb{d}a}{\mathbb{d}b}}{a^{2}}}}}}} & (8)\end{matrix}$where C_(g) is a scalar value known as the admissibility constant. It iswavelet type dependent and may be calculated from:

$\begin{matrix}{C_{g} = {\int_{0}^{\infty}{\frac{\left| {\hat{\psi}(f)} \right|^{2}}{f}{\mathbb{d}f}}}} & (9)\end{matrix}$

A continuous wavelet transform may be used to obtain characteristicmetrics of the respiratory components in a plethysmographic signal,which are, in turn, correlated with fluid responsiveness. In anembodiment, at least one suitable region, such as a ridge or a band ofthe scalogram may be analyzed to determine a level of fluidresponsiveness in a patient. For example, the amplitude modulation of apulse band ridge, such as pulse band ridge 154 in FIG. 5B, of thescalogram may be analyzed to determine information related to fluidresponsiveness. Changes in the amplitude modulation of theplethysmographic signal correlate with changes in the level of fluidresponsiveness.

The pulse band ridge manifests itself in the scalogram generated fromthe wavelet transform. The amplitude modulation of the plethysmographicsignal may be taken from the amplitude modulation of pulse band ridge.By measuring the amplitude variation of puke band ridge, the local pulsemodulation of the plethysmographic signal may be extracted. Thus, thelevel of fluid responsiveness may be captured by the relative pulseamplitude modulation (RPAM), which correlates the amplitude modulationof the plethysmographic signal to the level of fluid responsiveness.Higher values of RPAM may indicate greater levels of fluidresponsiveness of a patient. The RPAM may be approximated by theproperly scaled ratio A/M, where A may be peak-to-peak amplitudemodulation of the pulse band ridge and M may be a baseline signal suchas mean value of the pulse band ridge. The ratio A/M may be expressed asa percentage.

Other ratios or mathematical expressions may be used to define the RPAM.For example, A may be the standard deviation of the amplitude modulationof pulse band ridge, the median absolute value of the amplitudemodulation of pulse band ridge, any other suitable metric that expressesthe amplitude modulation of pulse band ridge to define the RPAM, or anycombination thereof. In addition, other characteristic baselines signalsor features may be used for M when defining the RPAM. For example, M maybe the lower bound of the signal interpolated from the troughs of pulseband ridge, the upper bound of the signal interpolated from the peaks ofpulse band ridge, any other suitable characteristic baseline signal, orany combination thereof.

In an embodiment, the carrier wave amplitude of the plethysmographicsignal may be analyzed to determine information related to fluidresponsiveness. Changes in the amplitude of the carrier wave during abreathing cycle correlate with changes in the level of fluidresponsiveness. For example, the carrier wave amplitude may be extractedfrom the amplitude of the breathing band, such as breathing band 156 inFIG. 5B, of the scalogram. The carrier wave may be indicative of venousreturn, and the breathing band manifests itself in the scalogramgenerated from the wavelet transform.

In another embodiment, the amplitude of the respiratory sinus arrhythmia(RSA) component of the plethysmographic signal may be analyzed todetermine information related to fluid responsiveness. Changes in theamplitude of the RSA component correlate with changes in the level offluid responsiveness. For example, the RSA component may be derived fromthe pulse band ridge, such as pulse band ridge 154 in FIG. 5(b), of thescalogram. Further, any band in the transform space indicative of pulseperiod may provide information for measuring RSA, such as a band at ascale above that of the pulse band, which, though of lower amplitude,may clearly indicate RSA. The amplitude modulation of the RSA maycorrelate with the amplitude modulation of the pulse band ridge. Bymeasuring the amplitude variation of the pulse band ridge, the localmodulation of the RSA waveform may be extracted. The RSA occursnaturally in the variation in the periodicity of the heart beat timingover the respiration cycle. The amplitude modulation of other componentsof the scalogram indicative of pulse period may be used to measure RSAin place of or in addition to the amplitude modulation of the pulse bandridge.

In other embodiments, a patient respiratory system 200 may operate underclosed-loop control to provide to delivery of a fluid therapy (e.g.,saline, blood, or other fluid) to a patient. FIG. 6 shows a system 200under control of a primary controller 202 that may include a closed-loopcontroller that cooperates with a monitor 14 to control delivery offluid therapy to the patient. The primary controller 202 may receiveinput from the monitor 14. The controller may also receive input from aventilator 204 or other device for determining a breathing cycle of apatient. Based on the plethysmographic waveform signal from the sensor12, the monitor 14 may determine a hypovolemia risk, such as a numericindicator, based on one or more features of the plethysmographicwaveform. The assessment of hypovolemia risk may be used by thecontroller 202 to control the fluid delivery device 206. It should beunderstood that while FIG. 6 depicts the controller 202 and the monitor14 as separate devices, the monitoring functions of monitor 14 and thecontroller functions of controller 202 may be incorporated into a singledevice in embodiments.

For example, the controller 202 may receive a request for increasedfluid from the monitor 14 when the monitor determines that the patientis at risk for hypovolemia. The fluid delivery device 206 may include aperistaltic pump or other type of pump attached to an automaticintravenous line to achieve the desired delivery rate of the fluid tothe patient. To control the rate at which the pump infuses the fluid,the speed of the pump may be controlled by the closed-loop controller202. The monitor 14 may continue to assess the patient's fluid conditionduring the fluid therapy. For example, if the plethysmographic waveformfeatures show a change in trend or absolute value that indicates thatthe risk of hypovolemia is reduced, the controller 202 may slow or stopdelivery of fluid from the fluid delivery device 206. If the monitor 14determines that the patient has not responded to fluid therapy, i.e.,the analyzed plethysmographic waveform features are consistent withhypovolemia, the controller 202 may generate a signal notifying acaregiver of prolonged hypovolemia.

While the disclosure may be susceptible to various modifications andalternative forms, specific embodiments have been shown by way ofexample in the drawings and will be described in detail herein. However,it should be understood that the disclosure is not intended to belimited to the particular forms disclosed. Rather, the disclosure is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the disclosure as defined by the following appendedclaims. Further, it should be understood that elements of the disclosedembodiments may be combined or exchanged with one another.

What is claimed is:
 1. A method comprising: receiving, by a processor, aphotoplethysmographic waveform signal from a sensor applied to apatient; determining, by the processor, a stroke volume over time forthe patient based on one or more features of the photoplethysmographicwaveform signal; correlating, by the processor, the stroke volume intime to an exhalation portion of a respiratory cycle of the patient; anddetermining, by the processor, that the patient is at risk of developinghypovolemia based on an increase in the stroke volume during theexhalation portion of the respiratory cycle relative to a first range orthreshold.
 2. The method of claim 1, wherein determining that thepatient is at risk of developing hypovolemia comprises determining anincreased risk based on a trend of an increase in the stroke volumeduring the exhalation portion of the respiratory cycle.
 3. The method ofclaim 1, wherein determining that the patient is at risk of developinghypovolemia comprises determining a reduced risk based on a relativedecrease in the stroke volume during the exhalation portion of therespiratory cycle after administration of fluid therapy.
 4. The methodof claim 1, further comprising triggering, by the processor, an alarm inresponse to determining that the patient is at risk of developinghypovolemia.
 5. The method of claim 1, further comprising: correlating,by the processor, the stroke volume in time to an inhalation portion ofthe respiratory cycle of the patient; determining, by the processor, afirst trend of an increase in the stroke volume during the exhalationportion of the respiratory cycle relative to the first range orthreshold; and determining, by the processor, a second trend of adecrease in the stroke volume during the inhalation portion of therespiratory cycle relative to a second range or threshold, whereindetermining that the patient is at risk of developing hypovolemiacomprises determining that the patient is at risk of developinghypovolemia based on a truth table, wherein the truth table comprises anindication of developing hypovolemia if the first and the second trendare present for a threshold time period.
 6. The method of claim 1,wherein determining that the patient is at risk of developinghypovolemia comprises counting a number of times of the increase in thestroke volume during the exhalation portion of the respiratory cyclerelative to the first range or threshold.
 7. The method of claim 1,further comprising: analyzing, by the processor, the stroke volume overtime after fluid therapy has been administered to the patient; anddetermining, by the processor, that a blood volume of the patient haschanged after administration of the fluid therapy.
 8. The method ofclaim 1, wherein the one or more features of the photoplethysmographicwaveform signal comprise a pulse amplitude.
 9. The method of claim 1,wherein the one or more features of the photoplethysmographic waveformsignal comprises at least one of a baseline shift, arrhythmic beats, abeat-to-beat variation, an area under a curve, or a pulse shape.
 10. Amethod comprising: receiving, by a processor, a photoplethysmographicwaveform signal from a sensor applied to a patient; determining, by theprocessor, a stroke volume of the patient over time based on one or morefeatures of the photoplethysmographic waveform signal; correlating intime, by the processor, the stroke volume to an exhalation portion of arespiratory cycle of the patient; determining, by the processor, abaseline value of the stroke volume over a period of time; determining,by the processor, a trend of the stroke volume during the exhalationportion of the respiratory cycle, wherein the trend is indicative of atrending change in the stroke volume relative to the baseline value, andwherein the trending change comprises an increase in the stroke volumerelative to the baseline value during the exhalation portion of therespiratory cycle that is greater than a range or threshold; anddetermining, by the processor, a risk of hypovolemia based on acomparison of the trend to the baseline value.
 11. The method of claim10, wherein determining the risk of hypovolemia comprises determining anincreased risk based on the trend of the increase in the stroke volumeduring the exhalation portion of the respiratory cycle.
 12. The methodof claim 10, wherein determining the stroke volume over time comprisesdetermining the stroke volume over time in a time window, wherein thetime window advances with respect to time.
 13. The method of claim 10,further comprising extracting, by the processor, one or more portions ofthe photoplethysmographic waveform signal correlated in time to theexhalation portion of the respiratory cycle, and wherein determining thetrend of the stroke volume during the exhalation portion of therespiratory cycle comprises determining the trend of the stroke volumein the extracted one or more portions of the photoplethysmographicwaveform signal correlated in time to the exhalation portion of therespiratory cycle.
 14. The method of claim 10, wherein determining therisk of hypovolemia comprises determining a hypovolemia indicator isgreater than a predetermined threshold value or outside of apredetermined range, the method further comprising triggering, by theprocessor, an alarm in response to determining the hypovolemia indicatoris greater than the predetermined threshold value or outside of thepredetermined range.
 15. The method of claim 10, further comprisingdisplaying an indicator related to the risk of hypovolemia on a display.16. A monitor comprising: an input configured to be coupled to a sensorapplied to a patient and to receive a photoplethysmographic waveformsignal from the sensor; a processor; and a memory storing instructionsthat, when executed on the processor, are configured to cause theprocessor to: determine a stroke volume over time for the patient basedon one or more features of the photoplethysmographic waveform signal;correlate the stroke volume in time to an exhalation portion of arespiratory cycle of the patient; and determine a risk of hypovolemiabased on an increase in the stroke volume during the exhalation portionof the respiratory cycle relative to a range or threshold.
 17. Themonitor of claim 16, wherein the instructions that cause the processorto determine the risk of hypovolemia, cause the processor to determinethe risk of hypovolemia based on the increase in the stroke volumeduring the exhalation portion of the respiratory cycle relative to abaseline stroke volume value that is greater than the range orthreshold.
 18. The monitor of claim 16, wherein the instructions thatcause the processor to determine the risk of hypovolemia, cause theprocessor to determine a trend of the increase in the stroke volume overtime during the exhalation portion of the respiratory cycle relative tothe range or threshold.
 19. The monitor of claim 18, wherein theinstructions further cause the processor to: correlate the stroke volumein time to an inhalation portion of the respiratory cycle of thepatient; determine an additional trend of a decrease in the strokevolume during the inhalation portion of the respiratory cycle relativeto an additional range or threshold; and determine the risk ofdeveloping hypovolemia based on the trend and the additional trend. 20.The monitor of claim 19, wherein the instructions that cause theprocessor to determine the risk of hypovolemia cause the processor tocompare the trend and the additional trend to a truth table, wherein thetruth table comprises an indication of developing hypovolemia based on adetermination that the trend and the additional trend are present for athreshold time period.
 21. The monitor of claim 16, wherein theinstructions further cause the processor to: analyze the stroke volumeover time after fluid therapy has been administered to the patient; anddetermine that a blood volume of the patient has changed afteradministration of fluid therapy.
 22. The monitor of claim 16, whereinthe one or more features of the photoplethysmographic waveform signalcomprises a pulse amplitude.
 23. The monitor of claim 16, wherein theone or more features of the photoplethysmographic waveform signalcomprises at least one of a baseline shift, arrhythmic beats, abeat-to-beat variation, an area under a curve, or a pulse shape.
 24. Themonitor of claim 16, wherein the range or threshold is empiricallydetermined based on clinical observations of patients with normal fluidstatus relative to hypovolemic patients.